Main
Daniel R. Kick, PhD
- Experience with statistics, programming, machine learning, and deep learning.
- Presented to scientific and general audiences over 25 times since 2016.
- Led 4 teaching assistants and mentored 7 research students.
- Developed statistical tool used by >700 students as of 2021.
Professional and Research Experience
Research Geneticist
Jacob Washburn Lab, USDA-ARS
N/A
Present - 2021
- Secured $225,000 to develop “Environmentally Aware Deep Learning Based Genomic Selection And Management Optimization For Maize Yield” from the National Institute of Food and Agriculture.
- Employed deep neural networks, machine learning models, and best linear unbiased predictors to improve corn yield prediction accuracy in diverse environments.
- Communicated with stakeholders via 11 presentations (5 national, 3 regional, 3 outreach).
- Mentored 2 students conducting a high throughput root phenotyping experiment.
- Created and taught a Python data visualization workshop titled “Tools and Techniques for a Jupyter Based Scientific Workflow”.
- Completed Software Carpentries instructor certification, taught R for Reproducible Scientific Analysis, and assisted in teaching Data Management with SQL.
- Designed and completed a professional development curriculum with the guidance of an industry scientist via the Bayer-University Mentoring Program.
- Served as a panel member on “Next-Generation Omics” at the 2022 University of Missouri Division of Biological Sciences Retreat.
Graduate Researcher
David Schulz Lab, University of Missouri
N/A
2021 - 2015
- Author on 6 publications: 4 original research and 2 eLife Insight publications.
- Assessed the efficacy of machine learning models to identify cell identity from mRNA and contig abundances. Applied cluster estimation, hyperparameter tuning, unsupervised machine learning, and supervised machine learning. Identified and learned needed skills primarily through self study. Collaborated with molecular biology project lead. (see Northcutt1, Kick1, et al. 2019).
- Defined research question and experiments. Developed novel approaches to quantify changes in cell activity.
- Collaborated with electrophysiologists, assisting with data analysis.
- Collaborated with computational neuroscientists, contributing domain expertise.
- Mentored 5 5 students and oversaw their projects.
- Communicated results through 18 presentations (6 national, 6 regional, 6 outreach).
- Served as a peer mentor of 3 PhD students in use of R for reproducible data analysis, created internal documents on same.
Lead Teaching Assistant, Animal Physiology Lab
Biological Sciences, University of Missouri
N/A
2021 - 2020
- Developed statistics web application used by more than 700 students as of 2021 with shiny (source, deployed) for data visualization, testing assumptions, and fitting frequentist, non-parametric, and Bayesian models.
- Led 4 Teaching Assistants and coordinated adaptation of lab curriculum to be fully online due to COVID-19 pandemic.
- Mentored next Lead Teaching Assistant, created documentation on best practices.
Teaching Assistant, Animal Physiology Lab
Biological Sciences, University of Missouri
N/A
2020 - 2018
- Updated and refined curriculum, delivered lectures and ensured experiments were conducted safely, and modeled student grade distributions to identify and adjust for differences in grading.
Curriculum Consultant, Animal Physiology Lab
Biological Sciences, University of Missouri
N/A
2018
- Redesigned course material to incorporate primary literature and data analysis.
Teaching Assistant, Animal Physiology Lab
Biological Sciences, University of Missouri
N/A
2016 - 2015
- Delivered weekly lectures, ensured experiments were conducted safely, provided timely feedback on assignments.
Undergradute Researcher
University of Missouri, University of Connecticut, and Truman State University
N/A
2013 - 2015
- Designed a hydroponic system for maize root phenotyping –(2014-2015), Quantified retinal minor splicisome expression using immunohistochemistry – (2014), Measured effectiveness of oligonucleotide treatment for spinal muscular atrophy in mice – (2013).
Honors and Awards (4/5)
NIFA Fellowship (AFRI EWD)
$225,000 awarded over two years to create and environmentally aware deep learning genomic selection models and prepare the recipient to transition into industry Grant Number 2023-67012-39485
N/A
2025 - 2023
J. Perry Gustafson Award for Outstanding Graduate Research in the Life Sciences
This $2,000 award is granted for the quality of their research and academic achievements.
N/A
2019
NIH T32 Training Grant Recipient
This provides a $27,000 yearly stipend and $750 yearly to facilitate presenting research at scientific conferences.
N/A
2018 - 2016
Cum Laude & President’s Recognition, Truman State University
N/A
N/A
2015
Selected Publications (Showing 3/7 Published, 1/2 in Review, 0/3 in Prep.)
Ensemble of BLUP, Machine Learning, and Deep Learning Models Predict Maize Yield Better than Each Model Alone
Daniel R. Kick, Jacob D. Washburn (\(bioR \chi iv\)) In Review at In silico Plants
N/A
2023
Yield Prediction Through Integration of Genetic, Environment, and Management Data Through Deep Learning
Daniel R. Kick, Jason G. Wallace, James C. Schnable, Judith M. Kolkman, Baris Alaca, Timothy M. Beissinger, David Ertl, Sherry Flint-Garcia, Joseph L. Gage, Candice N. Hirsch, Joseph E. Knoll, Natalia de Leon, Dayane C. Lima, Danilo Moreta, Maninder P. Singh, Teclemariam Weldekidan, Jacob D. Washburn G3: Genes, Genomes, Genetics
N/A
2023
Timing dependent potentiation and depression of electrical synapses contributes to network stability in the crustacean cardiac ganglion
Daniel R. Kick and David J. Schulz The Journal of Neuroscience
N/A
2022
Molecular profiling of single neurons of known identity in two ganglia from the crab Cancer borealis
Adam J. Northcutt1, Daniel R. Kick1, Adriane G. Otopalik, Benjamin M. Goetz, Rayna M. Harris, Joseph M. Santin, Hans A. Hofmann, Eve Marder, and David J. Schulz (1 denotes co-first authorship) Proceedings of the National Academy of Sciences
N/A
2019